BACKGROUND
[0001] Various types of devices, sensors and techniques exist for determining implicit and
explicit characteristics of people and places. Some systems use devices associated
with a particular user to sense or determine user specific information. Sensors in
or coupled to a mobile electronic device can sense various implicit indicators of
characteristics for a particular user. For example, sensors in a smartphone can sense
the physical properties, e.g., position, temperature, rate of motion, heartbeat, etc.,
of a particular user of the device to gather information that can imply characteristics
for that particular user. Other conventional mobile electronic device based systems
also gather information about particular users by providing mechanisms through which
a user can explicitly report user characteristics, e.g., age, mood, state of health,
weight, etc. For example, a smartphone can execute an application that prompts a user
to explicitly enter personal information. These types of mobile electronic devices
only gather information for one user at a time. That is, typically, each mobile electronic
device only gathers information about the owner or the current user of the device.
[0002] Other systems use stationary sensors, such as cameras, infrared imagers, microphones,
voice recognition, etc., to detect the characteristics of multiple people in a particular
area in proximity to the sensors. Such systems can analyze the physical properties
of the people to determine characteristics, e.g., mood, health, or demographic information,
for the people in that particular location. For example, systems exist that can determine
the mood, e.g., happy, content, sad, etc., of some portion of the people in a location
based on the physical properties, such as the degree to which a person is smiling,
for people who come within range of a particular sensor. Because the sensors in such
systems are stationary, the results are limited to locations in which the sensors
are installed. Furthermore, the resulting sample of a particular group or population
within range of the sensors is limited. The limited sampling of the group of people
can skew the results when interpolating, or otherwise determining, the mood or other
characteristics associated with a given location.
[0003] FIG. 1 illustrates a diagram of a particular region 100. The region 100 can include
a number of locations 120 in which various numbers of people 110 can be found. Some
of the locations 120 can include a stationary sensor (SS) 115. As shown, the distribution
of the stationary sensors 115 is limited to only a few of the possible locations 120.
Accordingly, only locations 120 that include a stationary sensor 115 are capable of
determining even an approximation of a characteristic, such as the mood, of some group
of people 110 in a particular location 120 or region 100. In the specific example
shown, only locations 120-1, 120-4, 120-6, 120-10, and 120-12 include stationary emotion
sensors 115. The other locations 120 have no means for reliably determining the characteristics
for those locations.
[0004] Furthermore, even locations 120 that are equipped with a stationary sensor 115 are
limited by the ability of the sensor to detect only a limited sample of the people
110 in the location. The limits of the stationary sensors 120 can be based on the
limits of the sensor in terms of range, speed, and accuracy. In addition, some people
may actively avoid the stationary sensors 120. For instance, a mood detecting camera
can be positioned at the front door of a given entertainment venue to capture the
facial expressions of people as they enter the venue, and another mood detecting camera
can be positioned near the performance stage of the same venue to capture facial expressions
of people as they watch a performance. The facial expressions captured by the mood
detecting camera at the front door of the venue might detect that a majority of the
people entering the venue are excited, and the facial expressions captured by the
mood detecting camera at the stage might detect that the majority of people near the
stage are happy. However, there may be other people, or even a majority of people
in the venue who may be bored, tired, or unhappy with the entertainment or the venue,
but the mood detecting cameras cannot capture an image of them. In such situations,
any interpolated result or conclusion as to the overall mood of the people in the
venue can be spurious, and thus, not represent the true mood or success of the venue
in entertaining its patrons.
US 2011/0301433 A1 discloses analysis of mental states is provided using web services to enable data
analysis.
EP 2333778 A1 discloses a digital data reproducing apparatus and a method for controlling the same.
US 2013/036080 A1 discloses A predictive tracking method and apparatus utilizing objective and subjective
data in order to predict user states.
SUMMARY OF INVENTION
[0005] The invention is defined by the appended set of claims. In the follow description,
any apparatus/method falling outside the scope of the appended set of claims should
be understood as an example useful for understanding the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006]
FIG. 1 illustrates conventional systems that use stationary sensor enabled electronic
devices for determining limited characteristics for select contexts.
FIG. 2A illustrates various types of sensor enabled electronic devices that can be
used in various embodiments of the present disclosure.
FIG. 2B is a block diagram of the sensor enabled electronic device that can be used
in various embodiments of the present disclosure.
FIG. 3 is a block diagram of a system for the deployment of multiple stationary and
mobile sensor enabled electronic devices for determining characteristics of various
contexts, according to various embodiments of the present disclosure
FIG. 4 illustrates various definitions of contexts, according to various embodiments
of the present disclosure.
FIG. 5 illustrates the flexible definitions of contexts, according to various embodiments
of the present disclosure.
FIG. 6 illustrates the combination of spatial and temporal components in a context,
according to various embodiments of the present disclosure.
FIG. 7 illustrates changes in population and context characteristics according to
changes in a temporal component of a context definition, according to various embodiments
of the present disclosure.
FIG. 8 is a flowchart of a method for defining contexts, according to various embodiments
of the present disclosure.
FIG. 9 is a flowchart of a method for determining context characteristics using sensor
data received from multiple sensor enabled electronic devices, according to various
embodiments of the present disclosure.
FIG. 10 illustrates emotion sensor data associated with various contexts, according
to embodiments of the present disclosure.
FIG. 11 illustrates tracking changes in a motion sensor data associated with various
contexts, according to embodiments of the present disclosure.
FIG. 12 illustrates trends of individual user emotion based on changes in context,
according to embodiments of the present disclosure.
FIG. 13 prediction of individual user emotions based on changes in context, according
to embodiments of the present disclosure.
FIG. 14 illustrates demographic sensor data associated with various contexts, according
to embodiments of the present disclosure.
FIG. 15 illustrates changes in demographic sensor data associated with various contexts,
according to various embodiments of the present disclosure
FIG. 16 illustrates health sensor data associated with various contexts, according
to embodiments of the present disclosure.
FIG. 17 illustrates changes in health sensor data associated with various contexts,
according to embodiments of the present disclosure.
DETAILED DESCRIPTION
[0007] Described herein are techniques for systems and methods for flexibly defining a particular
context and determining a characteristic for that context using distributed sensor
enabled electronic devices. In particular, embodiments of the present disclosure include
determining an emotion for a context using emotion sensors in stationary electronic
devices and mobile electronic devices. In the following description, for purposes
of explanation, numerous examples and specific details are set forth in order to provide
a thorough understanding of particular embodiments. Particular embodiments as defined
by the claims may include some or all of the features in these examples alone or in
combination with other features described below, and may further include modifications
and equivalents of the features and concepts described herein.
[0008] Various embodiments of the present disclosure include methods that can include receiving
emotion sensor data from multiple distributed emotion sensor enabled electronic devices.
The emotion sensor data can be based on information sensed by the distributed electronic
devices for a plurality of contexts. Such methods can include determining a first
context from multiple contexts, determining a first portion of the emotion sensor
data determined to be received from a portion of the distributed electronic devices,
wherein the portion of the emotion sensor data is based on information sensed for
the first context. The methods can also include determining a first emotion profile
for the first context based on the first portion of the emotion sensor data, wherein
the first emotion profile comprises a description of an emotion associated with the
first context.
[0009] Other embodiments of the present disclosure include non-transitory computer-readable
storage media containing instructions that, when executed, control a processor of
a computer system to be configured for receiving emotion sensor data from multiple
distributed electronic devices, wherein the emotion sensor data is based on information
sensed by the plurality of distributed electronic devices for a plurality of contexts.
Such embodiments may also include determining a first context from multiple contexts,
determining a first portion of the emotion sensor data determined to be received from
a first portion of the plurality of distributed electronic devices, wherein the first
portion of the emotion sensor data is based on information sensed for the first context,
and determining a first emotion profile for the first context based on the first portion
of the emotion sensor data. The first emotion profile may include a description of
an emotion associated with the first context.
[0010] Various other embodiments of the present disclosure include an electronic device
that includes a processor, an emotion sensor, an electronic communication interface,
and a non-transitory computer-readable storage medium. The non-transitory computer-readable
storage medium can contain instructions that when executed, control the processor
to be configured to activate the emotion sensor to determine an emotion sensor reading,
and determine context data for the emotion sensor reading. The context data describes
the circumstances in which the emotion sensor reading was determined. The instructions
can further control the processor to be configured to generate emotion sensor data
that includes the context data and the emotion sensor reading, send the emotion sensor
data to one or more remote service providers through the electronic communication
interface, and receive, from a first remote service provider in the one or more remote
service providers through the electronic communication interface, summary emotion
sensor data for a particular context. The summary emotion sensor data may include
emotion sensor data, received by the first remote service provider from a plurality
of other electronic devices, and determined to include context data that matches the
particular context.
[0011] Various embodiments of the present disclosure include systems, methods, and devices
for determining contexts and determining an emotion or an emotion profile for those
contexts using information received from multiple emotion sensor enabled electronic
devices. Contexts can be defined by a description that includes spatial and/or temporal
components. The spatial components can refer to various types of absolute and relative
location description systems, such as coordinate based maps systems and proximity
based location services. The temporal components can reference absolute and relative
time description systems. Such time description systems can include a start time and
date, a stop time and date, or a designation of some particular time period within
some proprietary or universal time keeping system. In some embodiments, the context
can be determined by the presence, concentration, or availability of emotion sensor
data for a particular time and place. Accordingly, contexts can be arbitrarily defined
as individual and composite combinations of time and location.
[0012] Once the context is selected or defined, all or some of the emotion sensor data received
from multiple electronic devices can be filtered or analyzed to determine some portion
of the emotion sensor data that includes or is associated with context data that matches
the selected context. The context data can include temporal and spatial components
that can describe the circumstances under which emotion sensor readings included in
the sensor data were sensed, recorded, or otherwise determined. In some embodiments,
the emotion sensor data include implicit indications of emotion and explicit descriptors
of emotions. The implicit descriptors can include processed or unprocessed emotion
sensor readings. Such sensor readings can be mapped to a particular emotion or emotion
profile. The explicit emotion descriptors can include one or more user reported points
of data regarding a specific or general emotional state for a context, e.g., an emotional
state reported by a user through a particular application, website, or social media
network. As used herein, the term "emotion sensor" can refer to any sensor that may
be used to sense information that can be used to infer an emotion or an emotion characteristic,
regardless of quality or accuracy. For example, an accelerometer might be used to
indicate the emotion of a person, or might be used in conjunction with the data from
other sensors to infer emotion of one or more people.
[0013] The emotion sensor data determined to be received from emotion sensor enabled electronic
devices that are or were in the context of interest can be analyzed to determine an
emotion profile for the context. There are many forms that the resulting emotion profiles
can take and can be based on the needs of the users or entities that will be consuming
or viewing the emotion profiles. For example, the emotion profile can include a complete
listing of all emotion sensor data for the context. In other embodiments, the emotion
profile can include summaries of the most frequent emotion indicators and descriptors
in the sensor data for the context. In one embodiment, the emotion profile can include
an aggregation of all of the emotion indicators into a single, aggregate emotion indicator.
Regardless of the format of the emotion profile, the profiles can be output over various
channels and lines of communications. For example, the emotion profiles and the related
contexts can be published to a website, sent as an email, broadcast in text messages,
or pushed using a Really Simple Syndication (RSS) feed.
[0014] Various embodiments of the present disclosure will now be described in more detail
with reference to specific devices, systems, and use cases.
Sensor Enabled Devices
[0015] A significant portion of users encounters or uses at least one electronic device
on a daily basis. Any or all such devices can be configured to include one or more
varieties of sensors. Fig. 2A illustrates several examples of sensor enabled electronic
devices 210. Some sensor enabled devices 210 are mobile devices (referred to as sensor
enabled mobile electronic devices 210) that many users carry nearly every day. These
devices include various types and brands of sensor enabled mobile telephones 210-1,
smart phones 210-2, tablet computers, and laptop computers, etc. While mobile computing
and communication devices are some of the most commonly used devices, there are other
sensor enabled mobile electronic devices 210 that are also often used. For instance,
various users carry sensor enabled pedometers, electronic music players (e.g., MP3)
210-3, watches 210-4, glasses, and, on occasion, specialty mobile electronic devices,
like self-guided position-sensitive museum tour devices. In addition, there are configurations
of mobile electronic devices in which one device can be tethered to or connected to
another device. For example, a watch 210-4 or watch 210-5, can be connected to a smart
phone 210-2 by a wired or wireless connection to share information, computing, networking,
or sensor resources.
[0016] Any of the coupled or individual sensor enabled mobile electronic devices 210 may
include one or more types of sensors, such as environmental, body, or location sensors.
The mobility of such devices provides for flexible deployment of sensors into a wide
range of contexts to determine various characteristics about those contexts. In addition,
there may be some contexts that are equipped with one or more types of sensor enabled
stationary devices (referred to as sensor enabled stationary electronic devices 210),
shown generically at 210-6, that can be installed or placed in various contexts for
detecting physical properties, e.g., temperature signatures, sound levels, facial
expressions, etc., of people and conditions in those contexts. The information determined
or sensed by stationary electronic devices 210-6 can be used independently or in conjunction
with the information collected from other mobile and stationary sensor enabled devices.
[0017] FIG. 2B illustrates a schematic of a sensor enabled electronic device 210 that can
be used in implementations of various embodiments of the present disclosure. As discussed
above, sensor enabled electronic device 210 can be a mobile or a stationary device.
Either type of electronic device can include an internal communication bus 219, through
which the constituent components of the electronic device 210 can communicate with
and/or control one another. In some embodiments, electronic device 210 can include
an internal sensor 215 and/or an external sensor 216. The sensors can include any
type of sensor capable of detecting a physical characteristic of a person, object,
or environment. In some embodiments, the external sensor 216 can be coupled to the
electronic device 210 by a wired or wireless connection. Accordingly, the external
sensor 216 can be configured to sense a region, object, or a part of a user's body
that is separate from the electronic device 210. For example, the external sensor
216 can be included in a wrist watch, a pair of spectacles/goggles, or a body monitor
that can be attached or affixed to a part of the user's body, e.g., a thermometer
or heart rate monitor.
[0018] Each of the sensors can be controlled by the processor 214 executing computer readable
code loaded into memory 213 or stored in the non-transitory computer readable medium
of data store 218. Readings sensed by the external sensor 216 and internal sensor
215 can be collected by the processor 214 and stored locally in the memory 213 or
the data store 218. In some embodiments, the readings from the external sensor 216
and/or the internal sensor 215 can be sent to remote service provider 230. In such
embodiments, electronic device 210 can include a communication interface 212 for translating
or converting the readings from the sensors from one format to another for transmission
using the communication transmitter/transceiver 212 and network 220. Accordingly,
electronic device 210 can be configured to communicate with network 220 and service
provider 230 using a variety of wired and wireless electronic communication protocols
and media. For example, electronic device 210 can be configured to communicate using
Ethernet, IEEE 802.11xx, worldwide interoperability for my quick access (WiMAX), general
packet radio service (GPRS), enhanced data rates for GSM evolution (EDGE), and long-term
evolution (LTE), etc. The readings from the sensors, or sensor data that includes
or is generated using the sensor readings, can be sent to the service provider 230
in real time. Alternatively, sensor readings or sensor data can be stored and/or sent
to the service provider 230 in batches or as network connectivity allows.
[0019] In some embodiments, the sensor enabled electronic device 210 can also include a
location determiner 217. The location determiner 217 can, through various methods
and technologies, e.g., global positioning systems (GPS), near field communication
(NFC), proximity sensors, etc., determine the location and movement of electronic
device 210. In some embodiments, the location determined by the location determiner
217 can be included or associated with sensor readings from the external sensor 216
and/or the internal sensor 215 in sensor data sent to service provider 230. As used
herein, the term sensor data is used to describe any data that includes or is associated
with sensor readings and/or user reported data. For example, in some embodiments,
sensor data can include the sensor readings and user reported data, along with the
time, date, and location at which the sensor readings were taken or the user reported
data was collected. The sensor data can also include any other conditions or exceptions
that were detected when the corresponding sensor data was determined.
Deployment of Sensor Enabled Devices
[0020] Fig. 3 illustrates a schematic of a system 300 that includes many sensor enabled
electronic devices 210 deployed in multiple contexts 410. The sensor enabled electronic
devices 210 can be implemented as stationary or mobile devices. As such, the stationary
devices can be explicitly associated with a particular location or event. For example,
sensor enabled electronic device 210-1 can be a stationary device equipped with a
camera, or other sensor, installed in a specific context, 410-1, such as a particular
location or in a particular vehicle (e.g., a bus, train, plane, ship, or other multi-person
conveyance).
[0021] In another example, some sensor enabled electronic devices 210 can be deployed passively.
For example, sensor enabled mobile devices 210 can be passively deployed into multiple
contexts by simply observing where users take their associated mobile devices. Passive
deployment of the sensor enabled electronic devices 210 refers to the manner in which
the devices are carried with users into whatever context the users choose. Accordingly,
there is no central entity that is directing where each sensor enabled mobile electronic
device 210 will be located or where it will go next. That decision is left up to individual
users of the sensor enabled mobile electronic devices 210. Accordingly, sensor enabled
mobile electronic devices 210-2 and 210-3 can be observed to be in a particular context
410-2, such as a location, at one time, but can then be observed in a different location
at another time. Various advantages that can be realized due to the passive deployment
of many sensor enabled mobile devices 210 will be described in reference to various
examples below.
[0022] In some embodiments, each sensor enabled electronic device 210 may include one or
more sensors or measurement devices for detecting, recording, or analyzing the characteristics
of one or more users, locations, or time periods. For example, each sensor enabled
electronic device 210 can include a light sensor, a microphone, decibel meter, an
accelerometer, a gyroscope, a thermometer, a camera, an infrared imager, a barometer,
an altimeter, a pressure sensor, a heart rate sensor, a galvanic skin response sensor,
a vibration sensor, a weight sensor, an odor sensor, or any other specialized or general
purpose sensor to detect characteristics of a particular user of a particular device
or other users, areas, or objects in the vicinity of the device. As discussed above,
the sensor enabled electronic devices 210 can also include location determination
capabilities or functionality, e.g., a global positioning system (GPS), proximity
detection, or Internet Protocol (IP) address location determination capabilities.
In such embodiments, sensor data collected by the various sensors can be associated
with a particular user and/or the particular location in which the sensor data was
recorded or otherwise determined. In one embodiment, the sensor data can also include
time and/or date information to indicate when the sensor data was captured or recorded.
As used herein, any data referring to time, date, location, events, and/or any other
spatial or temporal designation, can be referred to as context data. Accordingly,
any particular sensor data can be associated with and/or include context data that
describes the circumstances under which the sensor data was determined.
[0023] As shown in Fig. 2B, each of the sensor enabled electronic devices 210 can also include
electronic communication capabilities. Accordingly, the sensor enabled electronic
devices 210 can communicate with one another and various service providers 230 over
one or more electronic communication networks 220 using various types of electronic
communication media and protocols. The sensor enabled electronic devices 210 can send,
and the service providers 230 can receive, sensor data (SD) associated with various
particular users and contexts. The service providers 230, using one or more computer
systems, can analyze the sensor data to determine a characteristic of a particular
context.
[0024] In various embodiments of the present disclosure, the various service providers 230
can analyze the sensor data to determine mood, health, well-being, demographics, and
other characteristics of any particular context 410 for which the service providers
have sensor data. The service providers may then broadcast or selectively send the
determined characteristics data (CD) for a particular context 410 to one or more of
the sensor enabled electronic devices 210, as well as to other consumers. Such embodiments
will be described in more detail below.
Determining Contexts
[0025] As discussed herein, context is defined by a geographical area and time period at
various levels of granularity. Accordingly, context can include predefined locations,
such as a bar, restaurant, or amusement park during a particular predetermined time
period or event. When using predetermined or physical locations, the address or other
semantically meaningful designation of the location can be associated with a range
of coordinates that are observable by the sensor enabled devices. In contrast, a context
can be arbitrarily defined as any region or time period for which sensor data is available.
For example, a service provider 230 can filter sensor data received from multiple
sensor enabled electronic devices 210 for the sensor data associated with a specific
context of interest, e.g., a specific neighborhood, street, park, theater, nightclub,
vehicle, or event. Once the sensor data is filtered to isolate sensor data that includes
context data that matches or is associated with specific context 410 that the service
provider is interested in, the sensor readings in the sensor data can be analyzed
to determine or interpolate a particular characteristic for that particular context
410.
[0026] Fig. 4 illustrates how a region 400 can include a number of sub regions, or contexts
410, defined by a semantically meaningful geographic designation, like an address
or venue name. As depicted, region 400 can be segmented into a number of physical
locations 120 and contexts 410 by which the context data can be filtered or grouped.
Area 400 may represent a city, a neighborhood, a business district, an amusement park,
etc., or any sub region thereof. Region 400 can be further segmented into individual
and composite contexts 410. For example, context 410-1 can include a city block of
locations 120-1 through 120-5, e.g., a block of buildings or businesses, in a particular
neighborhood of region 400. In some embodiments, each location 120-1 to location 120-5
can be a particular context. However, as shown, the context 410-1 can comprise all
of the indoor space of locations 120-1 through 120-5, as well as any surrounding outdoor
space, i.e., outside courtyards, sidewalks, and streets. Accordingly, by defining
the area in and around locations 120-1 to 120-5 as a particular context 410-1, various
representations about that context can be determined by analyzing the sensor data
received from the sensor enabled devices determined to be in area 410-1. In one embodiment,
a server computer of a service provider 230 can filter the sensor data by the GPS
coordinates to determine which devices are or were in context 410-1. In other embodiments,
the service provider may reference a semantically meaningful geographic location from
social media check-in information included in the sensor data, e.g., a user may self-report
that he or she is dining at a restaurant at location 120-1 or exercising at a gym
120-4 inside context 410-1.
[0027] As shown, context 410-1 can also include a number of sub-contexts, such as contexts
410-2 and 410-3 that can be defined by a physical location and time period. For example,
context 410-2 can be defined by physical locations 120-3 and 120-3 between 9am and
8pm during some particular range of dates, e.g., a sale event. Similarly, context
410-3 can be defined by the physical location 120-5 on a specific night of a specific
day of the year, e.g., a special event like a wedding or a concert. Using the definitions
of the specific contexts of interest, particular embodiments can filter or sort the
received sensor data to isolate and analyze the relevant sensor readings to make determinations
about the characteristics of the people 110 in the particular contexts 410. For example,
the sensor data for context 410-2 may indicate that the majority of the people in
the context are "happy", while sensor data or user reported data for context 410-3
can indicate that the median age of the people in the context is 45 years old.
[0028] Similarly, context 410-4 can be defined to include location 120-6, the surrounding
area of location 120-6, and the stationary sensor 115-3 on a particular night of the
week, e.g., every Wednesday night. By including the stationary sensor 115-3, a server
computer analyzing the sensor data from sensor enabled mobile electronic devices 210
associated with the people 110 in context 410-4 can incorporate sensor data from the
stationary sensor 115-3. In such embodiments, the sensor data from sensor enabled
mobile electronic devices 210 or the stationary sensor 115 can be weighted according
to determined relevancy, reliability, recentness, or other qualities of the sensor
data. Additionally, the relative weights of the sensor data received from the mobile
and stationary devices can be based on predetermined thresholds regarding sample size.
If sensor data is received from some threshold number of sensor enabled mobile electronic
devices 210 in context 410-4, then the sensor data received from the stationary sensor
115-3 can have less weight in the conclusions about the characteristics of the context.
In contrast, if only a few people in context 410-4 who are carrying sensor enabled
mobile electronic devices 210 or there are only a few people in attendance, then the
sensor data from stationary sensor 115-3 can be more heavily weighted. Sample size
is just one example factor by which sensor data from mobile and stationary sensor
enabled devices can be weighted relative to one another. Weighting sensor data according
to various factors will be discussed below in more detail.
[0029] While the use of existing addresses and other semantically meaningful descriptions
is a convenient way to define a particular context, some embodiments of the present
disclosure allow for defining contexts that are not necessarily associated with a
particular physical location 120, such as a building or a venue. For example, context
410-5 can be defined in an open space that may or may not include a stationary sensor
115-5. For example, context 410-5 can include a parking lot or municipal park with
no definite physical boundaries. By filtering sensor data determined to include geographic
information for a particular area of interest, particular embodiments can flexibly
define contexts to include geographic locations of any size or shape. In some embodiments,
the geographic locations in a particular context can be defined by a range of GPS
coordinates.
[0030] Since a service provider can arbitrarily define a context, any previously defined
context can be redefined at any time as needed. Accordingly, contexts 410-1 and 410-2
shown in Fig. 4 can be reduced/merged into context 410-6 show in Fig. 5. Similarly,
context 410-5 shown in Fig. 4 can be divided into multiple contexts 410-9 and 410-10
as shown in Fig. 5 to obtain greater granularity in the sensor data associated with
the larger context 410-5. For instance, the context 410-5 may originally have been
defined around a large outdoor public space, but for a particular event, like a county
fair or festival, may be divided to be centered around featured events or exhibits,
such as a performance stage or art installation. Indoor spaces that define a context,
such as location 120-6, which defined context 410-4 in Fig. 4, can also be divided
into smaller contexts, like context 410-7 and 410-8as shown in Fig. 5. In addition,
new contexts can be added. Context 410-11 can be added in and around location 120-13
when a particular service provider or user requests or requires sensor data or a characteristic
determination for that particular context. For example, a new restaurant or bar may
have opened that an advertiser would like to know about.
[0031] As previously mentioned, the context can be defined by a combination of spatial and
temporal coordinates. FIG. 6 illustrates one particular context 410-14 that may include
designations of particular locations 120-11, 120-12, and 120-13, a particular day
615 of a particular month 610 at a particular time 620. As shown, context 410-14 can
include any number of people 110 who may or may not be carrying one or more sensor
enabled mobile electronic devices 210. Assuming that some portion of the people 110
are carrying sensor enabled mobile devices 210, then a service provider can receive
sensor data for context 410-14. In some embodiments, the service provider can filter
sensor data received from many sensor enabled mobile electronic devices 210 by analyzing
the context data included in the sensor data to determine which sensor data is associated
with or captured within the spatial and temporal boundaries of the context 410-14.
For example, context 410-14 can include an event, e.g., a grand opening, occurring
in multiple buildings 120-11, 120-12, and 120-13 on April 10, at 12:45 PM (-8 GMT).
The service provider can then filter the sensor data for context data that matches
the specific parameters with some degree of freedom, e.g., plus or minus 1 hour. The
service provider can then analyze the sensor readings in the sensor data determined
to match the specific parameters of the event to determine one or more characteristics
of the event. While analysis of the sensor data for individual contexts is helpful
for characterizing a particular context, it is often helpful or informative to understand
how various characteristics change from context to context.
[0032] In some embodiments, the service provider 230 can determine a difference between
a characteristic determined for one context and the characteristic determined at another
context. For example, the service provider 230 can compare the median age of people
110 in context 410-14, with the median age of people 110 in context 410-15 shown in
FIG. 7. In the specific examples shown in FIGs. 6 and 7, the physical locations 120-11,
120-12, and 120-13 of context 410-14 and context 410-15 are the same. However, the
time 720 and date 715 of context 410-15 are different from the time 620 and date 615
of context 410-14. By analyzing the difference in characteristics for each of the
contexts, the service provider can determine specific changes or trends. For example,
a server computer, based on analysis of sensor data determined to match contexts 410-14
and 410-15, can determine that the average age and the overall attendance increased
between April and June of a particular year. While the example shown in FIGs. 6 and
7 refers to two stationary locations, other embodiments of the present disclosure
include contexts that are defined by the interior space of multi-person conveyances,
such as planes, trains, boats, and buses.
[0033] Fig. 8 is a flowchart of a method for determining a particular context and sensor
data received from sensor enabled devices for that context. At 810, a service provider
230 can reference a semantically meaningful system of context descriptions. As described
herein, a context can be defined by a location, a time period, or a combination thereof.
Accordingly, the definition of a context may include a spatial component made in reference
to the semantically meaningful system of context descriptions. For example, the context
description can reference a map with a layout of predefined locations. The map can
represent a municipality with land lots or buildings identified by a system of street
addresses or lot numbers. Such municipal maps can include geographical survey data
that specifies the metes and bounds of various locations. Semantically meaningful
systems of context description can also include maps of individual properties, such
as amusement parks, shopping centers, fair grounds, universities, schools, tourist
destinations, etc. In such embodiments, a map of an individual property may include
absolute or relative positions of features, objects, or amenities on the property.
In addition, a semantically meaningful system of context description can also include
a temporal component, such as an event calendar or schedule of events. Accordingly,
the temporal component can be combined with the spatial component to describe a particular
time and a particular location.
[0034] In 820, the service provider 230 can select the context from the semantically meaningful
system of context descriptions. As discussed above, the selected context can include
a temporal and a spatial component. In 830, the service provider 230 may convert the
selected context from the semantically meaningful system of context descriptions to
an observable system of context descriptions. In such embodiments, the absolute or
relative temporal and spatial components of the selected context can be translated
into observable spatial components and/or observable temporal components. The observable
spatial and temporal components can reference a system that individual sensor enabled
electronic devices 210 can observe or sense. For example, the observable spatial components
can be defined according to systems for position location determination, e.g., global
positioning systems (GPS) or beacon proximity location systems. In one embodiment,
a street address for a particular public park can be translated into a set of geographic
coordinates that describe the boundaries of the park. Similarly, temporal components
can be defined according to a universal or common clock or calendar, such as Greenwich
Mean Time (GMT) or the Gregorian calendar. In such embodiments, the name of an event,
e.g., a concert can be translated into a period of time that includes a starting time
and date and ending time and date along with a particular venue location defined in
geographic coordinates. In other embodiments, each individual sensor enabled electronic
device 210 can translate the observable spatial and temporal components of the context
in which it determines sensor readings into a semantically meaningful system of context
descriptions. For example, a sensor enabled smartphone can take an ambient noise reading
at a particular set of coordinates as determined by the smartphone's GPS capabilities.
The smartphone can then reference an internal map of nearby music venues to determine
a particular venue based on the determined coordinate. The smartphone can then associate
the ambient noise reading with that venue. In such embodiments, the context data in
the sensor data can include the reference to the semantically meaningful system of
context descriptions.
[0035] In some embodiments, at 840, the service provider 230 can filter sensor data received
from multiple sensor enabled electronic devices 210 according the converted context
description, i.e., the observable spatial and temporal components of the context description.
Accordingly, filtering the sensor data may include determining sensor data that includes
context data that matches the converted context description.
[0036] On occasion, the sensor data determined to include context data that matches the
converted context description may not represent a satisfactory sample size. In such
scenarios, various embodiments of the present disclosure can trigger an alert to indicate
that the portion of the sensor data determined to match the converted context description
is insufficient for determining one or more characteristics for the context. When
there appears to be too little sensor data to determine a reliable characteristic
for the context, it is possible to increase the sample size by expanding the context
definition, e.g., increasing the geographic region and/or time period of the context.
If expanding the context definition does not result in a sufficient sample size, but
it is also possible to rely on or re-weight explicitly reported context characteristic
descriptions. For example, when the sample size of the sensor data is insufficient
to interpolate a reliable characteristic, then the interpolated characteristic can
be weighted less than any available user reported characteristic data when determining
combined characteristic data.
Determination of a Characteristic of a Context
[0037] Various embodiments of the present disclosure include systems and methods for determining
a particular characteristic of a context. For example, FIG. 9 is a flowchart of a
method 900 for determining one or more characteristics of a context using sensor data
from multiple sensor enabled electronic devices 210. As used herein, the sensor data
can include sensor readings as well as user reported data regarding a particular characteristic
of interest. In such embodiments, the sensor readings can represent implicit context
characteristic descriptions. Also, the user reported data can represent explicit context
characteristic descriptions. As shown, method 900 can begin at 910, in which a service
provider receives sensor data from multiple sensor enabled electronic devices. The
sensor data include implicit and explicit context characteristic data determined for
many different contexts. As discussed above, the sensor enabled electronic devices
210 can include both mobile and stationary electronic devices. At 920, the service
provider 230 may determine a portion of the sensor data that includes context data
that matches the context description for a particular selected context. In one embodiment,
received sensor data can be filtered to find only the sensor that includes context
data that indicates that the sensor readings or user reported data was determined
while the source sensor enabled electronic devices were in the selected context. In
one embodiment, user reported data can also include information and characteristics
reported by users using other devices and applications, such a web browser executed
on an internet-enable desktop computer or reported to a service provider operator
over a land line telephone.
[0038] At 930, once the portion of the sensor data associated with the selected context
is determined, the sensor readings and/or the user reported data can be analyzed to
determine a characteristic of interest for the selected context. Analyzing the sensor
data can include mapping the implicit context characteristic indications in the sensor
readings to corresponding context characteristics. The mapping from the implicit context
characteristic indications to the corresponding characteristics can be predetermined
and based on prior analysis performed by the service provider 230. Analyzing the sensor
data can also include comparing the mapped corresponding context characteristics with
the explicit context characteristic descriptions from the user reported data in the
sensor data. When both implicit and explicit context characteristic data are used,
the implicit and explicit components can be weighted according to observed or determined
reliability of the data. The reliability of the implicit and explicit components can
be based on the timeliness, frequency, or consistency of similar sensor data received
from each particular sensor enabled electronic device 210. Accordingly, sensor data
received from devices that are considered to be more reliable that other devices can
be given more weight when determining the context characteristic. Similarly, implicit
and explicit components of the context characteristic descriptions are weighted differently
based on perceived reliability. For example, if the sample size of the implicit components
is considered to be too small to be reliable, then the explicit components can be
given more weight. In contrast, if the explicit components seem to be spurious or
inconsistent with other available data, then the implicit components can be given
more weight when determining the characteristic of the context.
[0039] At 940, once the characteristic or characteristic profile for the selected context
is determined, it can be output for use by various users and entities. For example,
the form of the output characteristic can include a recommendation or alert regarding
the associated context sent to one or more mobile electronic devices. Similarly, the
output characteristic for the context can be published to a website, along with other
output characteristics for other contexts, or broadcast via email or by RSS. In some
embodiments, the output characteristic for the context can include tracking changes
or trends of the particular characteristic over a number of context parameters, e.g.,
over time. Accordingly, changes in the characteristic can be analyzed as a function
of a change in context. The change in context can include changes in the temporal
and/or spatial components of a particular context. For example, the mood, average
age, or wellness of a particular weekly event that may include occasional changes
in starting time and venue can be tracked as a function of start time or location.
In one embodiment, users can search for contexts with certain characteristics or browse
through contexts based on the context and/or the associated characteristics.
[0040] Specific examples of context characteristic determination with reference to emotion,
demographic, and health characteristics for particular contexts will be discussed
in more detail in reference to FIGs. 10 to 17 below.
Determination of an Emotion for a Context
[0041] Various embodiments of the present disclosure include systems and methods for determining
an emotion or emotion profile for particular contexts. FIG. 10 illustrates a scenario
1000 with two stationary location-based contexts 1005 and 1015, and one mobile location-based
context 1025, e.g., a public bus. In the particular example shown, context 1005 is
a building at the corner of an intersection and context 1015 is another building on
the same street. Each of the buildings can be associated with an address or a business
name included in a semantically meaningful system of context descriptions. Scenario
1000 also includes a context 1025 defined as the interior of a public or a private
bus. In some embodiments, context 1025 can be defined not only as the interior of
a particular bus, but as the interiors of some or all buses servicing a particular
route or line during some time period of the day.
[0042] A service provider 230 may receive emotion sensor data that includes implicit and
explicit indications of emotions from sensor enabled devices in any of the contexts
1005, 1015, and/or 1025. The implicit and explicit indications of emotions can be
mapped to or represent an emotional characteristic of one or more people in a particular
context. Such emotional characteristics can include any number of emotional states,
such as happiness, sadness, pensiveness, fear, anger, etc. In the example shown in
FIG. 10, the emotion sensor data can include indications of emotions that range from
sadness 1011, happiness 1012, and excitement 1013. While this particular example of
possible indications of emotions in the emotion sensor data is limited to three indications
of various emotions, other embodiments of the present disclosure can include fewer
or more possible indications of simple or complex emotions. The level of granularity
and range of possible emotions need not be limited.
[0043] By analyzing the emotion sensor data for the contexts, the service provider can determine
an associated emotion or emotion profile. The style and format of the reported emotion
or emotion profile for a particular context can be suited to the needs of the users
or other entities that will be using the emotion characterization of the context.
For example, when the emotion sensor data associated with context 1005 is analyzed,
it can be determined that there are more implicit and/or explicit indications of happiness
1012 and excitement 1013 than indications of sadness 1011. In this particular example,
the service provider 230 can determine that the context 1005 is trending as "happy".
In another embodiment, when the emotion sensor data associated with context 1015 is
analyzed, it can be determined that 40% of the people are happy, 40% of the people
are excited, and 20% of the people are sad. Similarly, by analyzing the emotion sensor
data associated with context 1025, it can be determined that the general mood of context
1025 is "sad".
[0044] In some embodiments, when it is determined that a particular context is associated
with a specific emotion, the emotion can be used as an indication that something is
occurring or has occurred, or to predict that something is about occur. For example,
when context 1025 is determined to be "sad", it can indicate that the bus has experienced
a traffic accident or is otherwise experiencing long delays. Similarly, when is determined
that all or a majority of the emotion sensor data for a particular context includes
indications of happiness, such information can be used as an indication that something
has gone favorably, e.g., a successful event is occurring. While characterizations
of the emotion for a context that includes static or one time summaries are useful
for some purposes, it is often useful to also include analysis of the changes in the
emotion or emotion profile for a context over one or more spatial or temporal components
of the context.
[0045] For example, FIG. 11 illustrates a scenario 1100 in which trends or changes in the
emotion sensor data can be observed. As shown, at time 1105-1, the emotions for contexts
1005, 1015, and 1025 can be characterized as shown in FIG. 11. However, after some
amount of time, e.g., 2 hours, at time 1105-2, the emotion sensor data received from
various sensor enabled electronic devices 210 in context 1005 can be analyzed to determine
that the context is trending "sad". This is because additional indications of a sad
emotion have been received in the last 2 hours. Also, at time 1105-2, the emotion
sensor data from devices determined to be in context 1015 can be analyzed to determine
that the context is 23% "happy", 66% "excited", and 11% "sad". In reference to the
context of the bus line or route 1025, the emotion sensor data can be analyzed to
determine that people on the bus are generally happy. The changes in the emotions
or emotion profiles for the contexts 1005, 1015, and 1025 can be tracked and the changes
or the trends can be included in the output regarding emotion or emotion profile for
each context. For example, a some particular time, context 1005 may be characterized
as "sad" but, based on the recent trends in the emotion sensor data for the context,
it may be experiencing a change in the predominate mood from sad and trending toward
"happy".
[0046] While trends in context emotion over time are useful for some analysis, some embodiments
include determining trends in context emotion according to changes in physical location.
For example, context 1025 of the bus can include not only the interior of the bus,
but can also include environments through which the bus travels. Accordingly, trends
in emotion can be tracked over changes in the buses position along its route. For
example, the emotion of the bus context 1025 can change from "happy" while the bus
is traveling through a nice part of town with little traffic to "sad" when the bus
starts traveling through another part of town with heavy traffic. Other aspects of
the context 1025 of the bus can also be tracked. For example, changes in drivers,
operators, tour guides, ambient music, dynamic advertising (video screen monitors
or public announcements), lighting, cleanliness, speed of travel, style of driving,
condition of the road, etc. can all be included in the context 1025 and cross-referenced
with the emotion sensor data received from the sensor enabled electronic devices to
determine the impact of such individual and combined changes on the mood of the context.
In particular example shown in FIG. 11, the bus context 1025 has been described in
detail, however other multi-person conveyances and transportation routes can also
be used to define a particular context. For example, other contexts can include stretches
of freeway, airline routes, train routes, subway lines, sections of road, etc. for
which emotion sensor data can be analyzed to determine an associated emotion or an
emotion profile.
[0047] Other embodiments of the present disclosure include tracking trends in emotion for
individual users. In such embodiments, sensor enabled mobile electronic devices 210
can be associated with particular users. Emotion sensor data, and other sensor data,
received from such devices can also be associated with individual users. As a user
moves from one context to the next context, changes in that user's emotion can be
tracked. For example, FIG. 12 shows emotion trend profiles 1110 that track the emotional
changes for individual users 110 as they move from one context to another. As shown,
profile 1110-1 tracks the emotion or mood of a user 1 as he or she goes from context
to context. Once some amount of emotion sensor data for a particular user 1 in a variety
of contexts is collected, various embodiments of the present disclosure can begin
predicting how a user's mood will change if he or she goes from one particular context
to another particular context.
[0048] FIG. 13 illustrates embodiments of the present disclosure can reference the emotion
trend profiles 1110 to predict a change in emotion for individual users in various
scenarios as they move from one type of contexts to another type of context. Based
on the emotion trend profile 1110 for each individual user, various predictions about
the change in a user's mood are represented according to shifts in context from a
starting context 120-X. If one particular user moves from starting context 120-X to
another context, such as 120-1, then, based on the emotion trend profile 1110 for
that user, it can be predicted that the user's mood will change or stay the same.
In the example shown, various users who begin as being happy in context 120-X can
be predicted to remain happy, become excited, or be saddened when moved into one of
the other contexts 120. Similarly, a user who begins as being sad in context 120-X
can be predicted to remain sad, or become happy or excited when moved into one of
the other contexts 120.
[0049] In some embodiments, the prediction of a particular change in a user's mood can include
consideration of current or historic determinations of the emotion of the context
into which the user is about to enter. For example, a prediction can be made about
whether a particular user will be happy if he or she attends a particular event at
a particular entertainment venue that is typically lively and happy. If trends in
the user's profile 1110 indicate a favorable mood change when going into such a context,
then a prediction can be made that the user will enjoy the change in context. Based
on such predictions, recommendations and/or alerts can be sent to the user via his
or her associated sensor enabled mobile electronic device 210 when it is determined
that the user is within some proximity to particular context.
Determination of Context Demographics
[0050] Various users and entities often find it useful to know about the demographics of
a particular context. Using demographic sensor data that can include implicit and
explicit indications of various demographic characteristics of people and environments
in particular contexts, various embodiments of the present disclosure can determine
a demographic or demographic profile for the contexts. For example, FIG. 14 illustrates
contexts 1005 and 1015 that include a spatial component, e.g., an address, and a time
component 1105-3, for which demographic sensor data has been received and/or collected.
The demographic sensor data can include indications of demographic characteristics
for people within each of the contexts. For the sake of clarity, the number of implicit
and explicit indications of demographic characteristics shown in FIG. 14 has been
limited. As shown, the demographic sensor data can include indications of a first
demographic characteristic 1401, a second demographic characteristic 1402, and a third
demographic characteristic 1403. While described generically as individually numbered
demographic characteristics, such demographic characteristics can include any individual
demographic characteristic or combination of demographic characteristics. For example,
the individually numbered demographic characteristics 1401, 1402, and 1403 can represent
any combination of quantifiable statistics for the people, such as age, sex, ethnicity,
race, sexual preference, social class, social scene, and any other implicitly or explicitly
determinable association with a particular group or classification.
[0051] By filtering the demographic sensor data determined to include or be associated with
context data that matches spatial and/or temporal components of contexts 1005 and
1015, various embodiments of the present disclosure can determine demographic profiles
for each context. The demographic profile for the context can include a complete listing
of the available demographic details for each person in that context. If less granularity
is required or desired, then a summary demographic profile can be created. For example,
based on the demographic sensor data, it can be determined that the demographics of
context 1005 are predominantly male. Similarly, it can be determined that the demographics
of context 1015 are predominantly female with an average age greater than 55. The
demographic profile for a particular context can then be output over various communication
channels, e.g., published to a website, sent to groups of subscribing users via email
or Short Message Service (SMS), or pushed to an application executed by mobile electronic
device.
[0052] Just as it is often useful to track changes in the emotion for a context, it can
also be useful to track changes in demographics for a context. FIG. 15 illustrates
a scenario 1500, in which changes in the demographic profile of contexts 1005 and
1015 are observed from time 1105-4 to time 1105-5. As shown, context 1005, e.g., the
interior and exterior region around a bar at a particular intersection, begins at
time 1105-4 being predominantly associated with demographic sensor data that includes
a particular demographic characteristic 1401. For example, demographic characteristic
1401 can be an indication of a male over the age of 40. Similarly, context 1015 at
time 1105-4 can be determined to be associated primarily with demographic sensor data
that includes indications of the particular demographic characteristic 1403, e.g.,
females around the age of 25. After some time period, at time 1105-5, the demographics
of contexts 1005 and 1015 may change. As illustrated, context 1005 may now also be
associated with demographic sensor data that includes various instances of demographic
characteristics 1401, 1403, 1405, 1406, 1407, and 1409. The demographic sensor data
of context 1015 at time 1105-5 can shift to include a predominant mixture of demographic
characteristic 1401 and 1402. Such shifts can indicate a change in the age, sex, ethnicity,
or other demographic characteristic of the inhabitants or patrons of a particular
context, i.e. the building or a business. The changes or trends in the demographic
or demographic profile of a context can then also be associated with the context and
output over various communication channels.
Determination of Health and Wellness of a Context
[0053] Through the use of various types of individual and group health sensors, various
embodiments of the present disclosure can determine the health and wellness for various
contexts. FIGs. 16 and 17 illustrate two scenarios 1600 and 1700 of the same geographic
region, e.g., a part of a town or city that includes a number of contexts. The contexts
can include the group of buildings in context 1605, an outdoor park in context 1615,
and a particular building in context 1625 during some particular time period, e.g.,
a week, month, or year. Accordingly, scenario 1600 in FIG. 16 can be associated with
one particular time period and scenario 1700 in FIG. 17 can be associated with another
particular time period. The time periods can overlap or be mutually exclusive.
[0054] By using the addresses, lot numbers, and/or the corresponding GPS coordinates of
the locations located in contexts of scenario 1600 to define the contexts, various
embodiments can filter health sensor data received from multiple sensor enabled electronic
devices 210 to determine the health sensor data that includes context data that matches
or is associated with the contexts of interest. The health sensor data determined
to include context data that matches each context can then be analyzed to determine
a health profile for the corresponding context.
[0055] Health sensor data received from health sensor enabled devices throughout scenario
1600 can be filtered to determine data that is associated with contexts 1615 and 1625,
and any other area or region or time frame that a user or entity might be interested
in as an individual or composite context. For example, context 1605 can be defined
by the areas in and around the buildings associated with a particular range of addresses.
The range of addresses can be used to determine the specific coordinates of the geographic
regions occupied by the buildings by referencing a geographic map or a third-party
mapping service. Context 1615 can be defined by the name of the park, which can be
used to reference some system of context descriptions, such as municipal survey data,
that defines the metes and bounds of the park with respect to geographical coordinates.
Context 1625 can be defined by the block and lot number of the building or the name
of the business that uses the building in context 1625. Such semantically meaningful
systems of context descriptions can then reference an observable system of context
descriptions to determine the limits of each context that will be observable by sensor
enabled devices. As with other embodiments of the present disclosure, health sensor
enabled devices can include GPS, proximity-based, and other location determination
and time determination capabilities. Accordingly, any health sensor readings obtained
by the health sensor enabled devices can be associated with context data that indicates
the contexts in which the health sensor readings were captured.
[0056] The health profiles for contexts 1605, 1615, and 1625 can include various details
about the health sensor data determined by health sensor enabled devices while the
devices were within each context. For example, the health profile for contexts 1605,
1615, and 1625 can include a complete listing of all implicit health sensor data and
explicit user reported health data, such as health indications 1601, 1602, and 1603.
In other embodiments, health profiles can include a summary or average of the health
indications present in the sensor data for a particular context 1605. In general,
the health profile for each context can be customized to analyze the health indications
according to the needs of a particular entity or user.
[0057] While the health indications 1601, 1602, and 1603 are listed as generic indications
or descriptors of health of one or more people within the context, e.g., A, B, and
C, embodiments of the present disclosure include any and all health and/or wellness
descriptors determinable, observable, or inferable by health sensor enabled devices.
For example, descriptors of health can include a description of body mass index (BMI),
weight, blood pressure, blood sugar, heart rate, temperature, stress, or body fat
content. Such descriptions can include numerical indexes or general/layman terms,
such as underweight, normal weight, overweight, obese, and morbidly obese. Other descriptors
of health can include explicit user reported data, such as vaccination status, mental
health status, feelings of wellness, disease and health history, etc. In some embodiments,
the health sensor data can also include environmental sensor readings that describe
or indicate the presence of toxins, poisons, pollution, and other helpful or harmful
factors that can impact the health of individuals that inhabit or use a particular
context.
[0058] Accordingly, the health descriptors from the health sensor data associated with a
context can be analyzed to produce default or custom health profiles for that context.
For example, context 1625 can include a restaurant. The summary of the health sensor
data that includes health indications 1601, 1602, 1603, and 1607, can be included
in the health profile of the restaurant, e.g., overweight people eat at the restaurant.
Similarly, the health profile associated with context 1615, that includes outdoor
park space, can indicate that people who use the park are generally physically fit
and have low cholesterol.
[0059] While snapshot or cumulative health profiles for each context can be useful for various
purposes, is often useful to also track the changes in health profiles and/or health
descriptors for specific contexts according to spatial or temporal changes. As discussed
above in reference to emotion and demographic changes for specific contexts, embodiments
of the present disclosure can also track changes in health for contexts. For example,
scenario 1700 of FIG. 17 illustrates changes in health for contexts 1605, 1615, and
1625 relative to scenario 1600 of Fig. 16. Specifically, the health profile associated
with context 1605 may change only slightly, if at all, if only limited changes in
the associated health descriptors in the health sensor data are observed between scenario
1600 and 1700. Meanwhile, the health profiles associated with context 1615 and 1625
may change dramatically due to the observed or determined differences in health descriptors
in the health sensor data associated with those contexts. Whereas the health profile
associated with context 1615 in scenario 1600 may have indicated that physically fit
people frequented the park, the health profile associated with the context 1615 in
scenario 1700 may indicate that the park is now frequented by people who smoke cigarettes
or drink alcohol on a regular basis. In contrast to the apparent decline in the health
of context 1615, the health profile of the restaurant in context 1625 may change for
the better. For example, the health indicators 1601 associated with context 1625 in
scenario 1700 may now indicate that mostly physically fit people with low blood pressure
patronize the restaurant.
[0060] As with other characteristic profiles, the health profiles of the various contexts
can be output over various communication channels and methods. For example, the health
profile for the particular restaurant in context 1625 can be included in a restaurant
review. Outputting the health profile for the context 1605 that includes a number
of buildings in a particular neighborhood can include generating a recommendation
or an alert to real estate agents or public health department officials that the health
for the context is in decline or is improving. Health profiles that indicate a decline
or an increase in the general health or specific health characteristics of individuals
who inhabit or use particular contexts can be used to indicate, analyze, and predict
various environmental changes, epidemic changes, population changes, and other changes
occurring within a context.
[0061] Particular embodiments may be implemented in a non-transitory computer-readable storage
medium for use by or in connection with the instruction execution system, apparatus,
system, or machine. The computer-readable storage medium contains instructions for
controlling a computer system to perform a method described by particular embodiments.
The computer system may include one or more computing devices. The instructions, when
executed by one or more computer processors, may be operable to perform that which
is described in particular embodiments.
[0062] As used in the description herein and throughout the claims that follow, "a", "an",
and "the" includes plural references unless the context clearly dictates otherwise.
Also, as used in the description herein and throughout the claims that follow, the
meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0063] The above description illustrates various embodiments along with examples of how
aspects of particular embodiments may be implemented. The above examples and embodiments
should not be deemed to be the only embodiments, and are presented to illustrate the
flexibility and advantages of particular embodiments as defined by the following claims.
Based on the above disclosure and the following claims, other arrangements, embodiments,
implementations may be employed without departing from the scope hereof as defined
by the claims.
1. A method comprising:
receiving, by a computer system, emotion sensor data for a plurality of contexts from
a plurality of distributed electronic devices (210), wherein each of the plurality
of contexts corresponds to one or more of a geographical area and a time period, wherein
the emotion sensor data comprises implicit emotion indicators based on information
sensed by the plurality of distributed electronic devices for the plurality of contexts,
wherein the emotion sensor data further comprises explicit emotion descriptors based
on user reported descriptions of emotions for the plurality of contexts;
determining, by the computer system, a first context in the plurality of contexts;
determining, by the computer system, a first portion of the emotion sensor data determined
to be received from a first portion of the plurality of distributed electronic devices,
wherein the first portion of the emotion sensor data is based on information sensed
for the first context;
determining, by the computer system, a first emotion profile for the first context
based on the first portion of the emotion sensor data, the determining comprising
weighting the implicit emotion indicators differently from the explicit emotion descriptors
based at least on perceived reliability of the implicit emotion indicators and the
explicit emotion descriptors,
wherein the first emotion profile comprises a description of a first emotion associated
with the first context;
tracking, using an emotion trend profile (1110), an emotional change associated with
an individual user moving from one of the plurality of contexts to another of the
plurality of contexts; and
predicting, using the emotion trend profile and the first emotion profile, a predicted
emotional change associated with said user moving from one of the plurality of contexts
to the first context.
2. The method of claim 1, wherein the first portion of the emotion sensor data comprises
a plurality of emotion sensor readings from information sensed by the first portion
of the plurality of distributed electronic devices (210), wherein the plurality of
emotion sensor readings include an emotion indicator for a plurality of people associated
with the first context.
3. The method of claim 1, wherein the plurality of distributed electronic devices (210)
comprises a plurality of mobile electronic devices.
4. The method of claim 3, wherein the plurality of distributed electronic devices (210)
further comprises a plurality of stationary electronic devices configured to sense
emotional data for a plurality of particular locations.
5. The method of claim 4, wherein the first portion of the plurality of distributed electronic
devices (210) comprises a portion of the plurality of mobile electronic devices and
a portion of the plurality of stationary electronic devices.
6. The method of claim 5, wherein emotion sensor data determined to be received from
the plurality of mobile electronic devices is weighted differently from emotion sensor
data determined to be received from the plurality of stationary electronic devices
in determining the first emotion profile.
7. The method of claim 1, further comprising:
determining, by the computer system, a second context in the plurality of contexts;
determining, by the computer system, a second portion of the emotion sensor data determined
to be received from a second portion of the plurality of distributed electronic devices
(210); and
determining, by the computer system, a second emotion profile for the second context
based on the second portion of the emotion sensor data,
wherein the second emotion profile comprises a description of a second emotion associated
with the second context.
8. The method of claim 7, wherein the first context comprises a first time period during
an event, and the second context comprises a second time period during the event.
9. The method of claim 7, wherein the first context comprises a first location, and the
second context comprises a second location.
10. The method of claim 7, further comprising:
determining, by the computer system, a difference between the first emotion profile
and the second emotion profile, wherein in the difference between the first emotion
profile and the second emotion profile describes an emotion profile trend related
to the first context and the second context; and
outputting, by the computer system, the difference between the first emotion profile
and the second emotion profile.
11. The method of claim 7, wherein the second portion of the plurality of distributed
electronic devices (210) is determined from the first portion of the plurality of
distributed electronic devices.
12. The method of claim 11, wherein the first context comprises a first location, and
the second context comprises a second location, and wherein a portion of the first
location comprises the second location.
13. The method of claim 1, wherein the first context comprises event data at which the
first portion of the plurality of distributed electronic devices (210) were present
to sense the information on which the emotion sensor data is based.
14. The method of claim 13, wherein the event data comprises a particular time period
associated with a particular location.
15. The method of claim 1, wherein the first context comprises a dynamically determined
geographical region in which the first portion of the plurality of distributed electronic
devices (210) were located to sense the information on which the emotion sensor data
is based.
16. The method of claim 1, further comprising receiving, by the computer system, event
calendar data, wherein the event calendar data comprises a plurality of dates and
a plurality of associated locations, and wherein determining the first context comprises
referencing the event calendar data.
17. The method of claim 1, further comprising outputting, by the computer system, the
first emotion profile.
18. A non-transitory computer-readable storage medium containing instructions that, when
executed, control the computer of an electronic device to perform the steps of :
receiving emotion sensor data from a plurality of distributed electronic devices (210)
for a plurality of contexts, wherein each of the plurality of contexts corresponds
to one or more of a geographical area and a time period, wherein the emotion sensor
data comprises implicit emotion indicators based on information sensed by the plurality
of distributed electronic devices for a plurality of contexts, and wherein the emotion
sensor data further comprises explicit emotion descriptors based on user reported
descriptions of emotions for the plurality of contexts;
determining a first context in the plurality of contexts;
determining a first portion of the emotion sensor data determined to be received from
a first portion of the plurality of distributed electronic devices, wherein the first
portion of the emotion sensor data is based on information sensed for the first context;
determining a first emotion profile for the first context based on the first portion
of the emotion sensor data, the determining comprising weighting the implicit emotion
indicators differently from the explicit emotion descriptors based at least on perceived
reliability of the implicit emotion indicators and the explicit emotion descriptors,
wherein the first emotion profile comprises a description of an emotion associated
with the first context;
tracking, using an emotion trend profile (1110), an emotional change associated with
an individual moving from one of the plurality of contexts to another of the plurality
of contexts; and
predicting, using the emotion trend profile and the first emotion profile, a predicted
emotional change associated with an individual user moving from one of the plurality
of contexts to the first context.
19. An electronic device comprising a computer and the non-transitory computer-readable
storage medium of claim 18.
1. Verfahren, Folgendes umfassend:
Empfangen, durch ein Computersystem, von Emotionserkennungsdaten für eine Vielzahl
von Kontexten aus einer Vielzahl von verteilten elektronischen Vorrichtungen (210),
wobei jeder der Vielzahl von Kontexten einem oder mehreren von einem geografischen
Gebiet und einem Zeitraum entspricht, wobei die Emotionserkennungsdaten implizite
Emotionsindikatoren auf der Grundlage von Informationen umfassen, die von der Vielzahl
von verteilten elektronischen Vorrichtungen für die Vielzahl von Kontexten erfasst
werden, wobei die Emotionserkennungsdaten ferner explizite Emotionsdeskriptoren auf
der Grundlage der von dem Benutzer berichteten Emotionsbeschreibungen für die Vielzahl
von Kontexten umfassen;
Bestimmen, durch das Computersystem, eines ersten Kontexts aus der Vielzahl von Kontexten;
Bestimmen, durch das Computersystem, eines ersten Abschnitts der Emotionserkennungsdaten,
für welche festgestellt wird, dass sie von einem ersten Abschnitt der Vielzahl von
verteilten elektronischen Vorrichtungen empfangen werden, wobei der erste Abschnitt
der Emotionserkennungsdaten auf für den ersten Kontext erfassten Informationen basiert;
Bestimmen, durch das Computersystem, eines ersten Emotionsprofils für den ersten Kontext
auf der Grundlage des ersten Abschnitts der Emotionserkennungsdaten, wobei das Bestimmen
das unterschiedliche Gewichten der impliziten Emotionsindikatoren in Bezug auf die
expliziten Emotionsdeskriptoren mindestens auf der Grundlage der wahrgenommenen Zuverlässigkeit
der impliziten Emotionsindikatoren und der expliziten Emotionsdeskriptoren umfasst,
wobei das erste Emotionsprofil eine Beschreibung einer ersten Emotion umfasst, die
dem ersten Kontext zugeordnet ist;
Verfolgen, unter Verwendung eines Emotionstrendprofils (1110), einer emotionalen Änderung,
die einem einzelnen Benutzer zugeordnet ist, der sich von einem der Vielzahl von Kontexten
zu einem anderen der Vielzahl von Kontexten bewegt; und
Vorhersagen, unter Verwendung des Emotionstrendprofils und des ersten Emotionsprofils,
einer vorhergesagten emotionalen Änderung, die damit verbunden ist, dass sich der
Benutzer von einem der Vielzahl von Kontexten in den ersten Kontext bewegt.
2. Verfahren nach Anspruch 1, wobei der erste Abschnitt der Emotionserkennungsdaten eine
Vielzahl von Emotionenerkennungs-Ablesungen von Informationen umfasst, die von dem
ersten Abschnitt der Vielzahl von verteilten elektronischen Vorrichtungen (210) erfasst
wurden, wobei die Vielzahl von Emotionenerkennungs-Ablesungen einen Emotionsindikator
für eine Vielzahl von Personen enthalten, die dem ersten Kontext zugeordnet sind.
3. Verfahren nach Anspruch 1, wobei die Vielzahl von verteilten elektronischen Vorrichtungen
(210) eine Vielzahl von mobilen elektronischen Vorrichtungen umfassen.
4. Verfahren nach Anspruch 3, wobei die Vielzahl von verteilten elektronischen Vorrichtungen
(210) ferner eine Vielzahl von stationären elektronischen Vorrichtungen umfassen,
die dafür konfiguriert sind, emotionale Daten für eine Vielzahl von bestimmten Positionen
zu erfassen.
5. Verfahren nach Anspruch 4, wobei der erste Abschnitt der Vielzahl von verteilten elektronischen
Vorrichtungen (210) einen Abschnitt der Vielzahl von mobilen elektronischen Vorrichtungen
und einen Abschnitt der Vielzahl von stationären elektronischen Vorrichtungen umfasst.
6. Verfahren nach Anspruch 5, wobei die Emotionserkennungsdaten, für welche bestimmt
wird, dass sie von der Vielzahl von mobilen elektronischen Vorrichtungen empfangen
werden, in Bezug auf die Emotionserkennungsdaten, für welche bestimmt wird, dass sie
von der Vielzahl von stationären elektronischen Vorrichtungen empfangen werden, bei
dem Bestimmen des ersten Emotionsprofils unterschiedlich gewichtet werden.
7. Verfahren nach Anspruch 1, ferner Folgendes umfassend:
Bestimmen, durch das Computersystem, eines zweiten Kontexts aus der Vielzahl von Kontexten;
Bestimmen, durch das Computersystem, eines zweiten Abschnitts der Emotionserkennungsdaten,
für welche festgestellt wird, dass sie von einem zweiten Abschnitt der Vielzahl von
verteilten elektronischen Vorrichtungen (210) empfangen werden; und
Bestimmen, durch das Computersystem, eines zweiten Emotionsprofils für den zweiten
Kontext auf der Grundlage des zweiten Abschnitts der Emotionserkennungsdaten, wobei
das zweite Emotionsprofil eine Beschreibung einer zweiten Emotion umfasst, die dem
zweiten Kontext zugeordnet ist.
8. Verfahren nach Anspruch 7, wobei der erste Kontext einen ersten Zeitraum während eines
Ereignisses und der zweite Kontext einen zweiten Zeitraum während des Ereignisses
umfasst.
9. Verfahren nach Anspruch 7, wobei der erste Kontext eine erste Position und der zweite
Kontext eine zweite Position umfasst.
10. Verfahren nach Anspruch 7, ferner Folgendes umfassend:
Bestimmen, durch das Computersystem, einer Differenz zwischen dem ersten und dem zweiten
Emotionsprofil, wobei die Differenz zwischen dem ersten und dem zweiten Emotionsprofil
ein Emotionsprofiltrend in Bezug auf den ersten und den zweiten Kontext beschreibt;
und
Ausgeben, durch das Computersystem, der Differenz zwischen dem ersten und dem zweiten
Emotionsprofil.
11. Verfahren nach Anspruch 7, wobei der zweite Abschnitt der Vielzahl von verteilten
elektronischen Vorrichtungen (210) vom ersten Abschnitt der Vielzahl von verteilten
elektronischen Vorrichtungen bestimmt wird.
12. Verfahren nach Anspruch 11, wobei der erste Kontext eine erste Position und der zweite
Kontext eine zweite Position umfasst, und wobei ein Abschnitt der ersten Position
die zweite Position umfasst.
13. Verfahren nach Anspruch 1, wobei der erste Kontext das Erfassen von Daten eines Ereignisses
umfasst, bei dem der erste Abschnitt der Vielzahl von verteilten elektronischen Vorrichtungen
(210) vorhanden war, um Informationen, auf der die Emotionserkennungsdaten basieren,
zu erfassen.
14. Verfahren nach Anspruch 13, wobei die Ereignisdaten einen bestimmten Zeitraum umfassen,
der einer bestimmten Position zugeordnet wird.
15. Verfahren nach Anspruch 1, wobei der erste Kontext eine dynamisch bestimmte geografische
Region umfasst, in welcher der erste Abschnitt der Vielzahl von verteilten elektronischen
Vorrichtungen (210) angeordnet wurde, um die Informationen zu erfassen, worauf die
Emotionserkennungsdaten basieren.
16. Verfahren nach Anspruch 1, ferner umfassend das Empfangen, durch das Computersystem,
von Ereigniskalenderdaten, wobei die Ereigniskalenderdaten eine Vielzahl von Daten
und eine Vielzahl von zugeordneten Positionen umfasst, und wobei das Bestimmen des
ersten Kontexts das Referenzieren der Ereigniskalenderdaten umfasst.
17. Verfahren nach Anspruch 1, ferner umfassend das Ausgeben, durch das Computersystem,
des ersten Emotionsprofils.
18. Nichtflüchtiges computerlesbares Speichermedium mit Anweisungen, die bei Ausführung
den Computer einer elektronischen Vorrichtung steuern, um die folgenden Schritte auszuführen:
Empfangen von Emotionserkennungsdaten aus einer Vielzahl von verteilten elektronischen
Vorrichtungen (210) für eine Vielzahl von Kontexten, wobei jeder der Vielzahl von
Kontexten einem oder mehreren von einem geografischen Gebiet und einem Zeitraum entspricht,
wobei die Emotionserkennungsdaten implizite Emotionsindikatoren auf der Grundlage
von Informationen umfassen, die von der Vielzahl von verteilten elektronischen Vorrichtungen
für die Vielzahl von Kontexten erfasst werden, und wobei die Emotionserkennungsdaten
ferner explizite Emotionsdeskriptoren auf der Grundlage der von dem Benutzer berichteten
Emotionsbeschreibungen für die Vielzahl von Kontexten umfassen; Bestimmen eines ersten
Kontexts in der Vielzahl von Kontexten;
Bestimmen eines ersten Abschnitts der Emotionserkennungsdaten, für welche festgestellt
wird, dass sie von einem ersten Abschnitt der Vielzahl von verteilten elektronischen
Vorrichtungen empfangen werden, wobei der erste Abschnitt der Emotionserkennungsdaten
auf für den ersten Kontext erfassten Informationen basiert;
Bestimmen eines ersten Emotionsprofils für den ersten Kontext auf der Grundlage des
ersten Abschnitts der Emotionserkennungsdaten, wobei das Bestimmen das unterschiedliche
Gewichten der impliziten Emotionsindikatoren in Bezug auf die expliziten Emotionsdeskriptoren
mindestens auf der Grundlage der wahrgenommenen Zuverlässigkeit der impliziten und
der expliziten Emotionsdeskriptoren umfasst, wobei das erste Emotionsprofil eine Beschreibung
einer Emotion umfasst, die dem ersten Kontext zugeordnet ist;
Verfolgen, unter Verwendung eines Emotionstrendprofils (1110), einer emotionalen Änderung,
die einem Individuum zugeordnet ist, das sich von einem der Vielzahl von Kontexten
zu einem anderen der Vielzahl von Kontexten bewegt; und
Vorhersagen, unter Verwendung des Emotionstrendprofils und des ersten Emotionsprofils,
einer vorhergesagten emotionalen Änderung, die damit verbunden ist, dass sich ein
einzelner Benutzer von einem der Vielzahl von Kontexten in den ersten Kontext bewegt.
19. Elektronische Vorrichtung, umfassend einen Computer und das nichtflüchtige computerlesbare
Speichermedium nach Anspruch 18.
1. Procédé comprenant :
la réception, par un système informatique, des données de capteur d'émotion pour une
pluralité de contextes à partir d'une pluralité de dispositifs électroniques distribués
(210), chacun de la pluralité de contextes correspondant à un ou plusieurs d'une zone
géographique et d'une période, les données de capteur d'émotion comprenant des indicateurs
d'émotion implicites en fonction des informations détectées par la pluralité de dispositifs
électroniques distribués pour la pluralité de contextes, les données de capteur d'émotion
comprenant en outre des descripteurs d'émotion explicites en fonction des descriptions
d'émotions rapportées par l'utilisateur pour la pluralité de contextes ;
la détermination, par le système informatique, d'un premier contexte dans la pluralité
de contextes ;
la détermination, par le système informatique, d'une première partie des données de
capteur d'émotion déterminée pour être reçue d'une première partie de la pluralité
de dispositifs électroniques distribués, la première partie des données de capteur
d'émotion étant basée sur des informations détectées pour le premier contexte ;
la détermination, par le système informatique, d'un premier profil d'émotion pour
le premier contexte en fonction de la première partie des données de capteur d'émotion,
la détermination comprenant la pondération des indicateurs d'émotion implicites différemment
des descripteurs d'émotion explicites en fonction au moins de la fiabilité perçue
de l'émotion implicite des indicateurs et des descripteurs d'émotion explicites, le
premier profil d'émotion comprenant une description d'une première émotion associée
au premier contexte ;
le suivi, à l'aide d'un profil de tendance émotionnelle (1110), d'un changement émotionnel
associé à un utilisateur individuel passant d'un contexte parmi la pluralité de contextes
à un autre parmi la pluralité de contextes ; et
la prédiction, à l'aide du profil de tendance émotionnelle et du premier profil émotionnel,
d'un changement émotionnel prédit associé audit utilisateur passant d'un contexte
parmi la pluralité de contextes au premier contexte.
2. Procédé selon la revendication 1, dans lequel la première partie des données de capteur
d'émotion comprend une pluralité de lectures de capteur d'émotion à partir d'informations
détectées par la première partie de la pluralité de dispositifs électroniques distribués
(210), la pluralité de lectures de capteur d'émotion comportant un indicateur d'émotion
pour une pluralité de personnes associées au premier contexte.
3. Procédé selon la revendication 1, dans lequel la pluralité de dispositifs électroniques
distribués (210) comprend une pluralité de dispositifs électroniques mobiles.
4. Procédé selon la revendication 3, dans lequel la pluralité de dispositifs électroniques
distribués (210) comprend en outre une pluralité de dispositifs électroniques fixes
configurés pour détecter des données d'émotion pour une pluralité d'emplacements particuliers.
5. Procédé selon la revendication 4, dans lequel la première partie de la pluralité de
dispositifs électroniques distribués (210) comprend une partie de la pluralité de
dispositifs électroniques mobiles et une partie de la pluralité de dispositifs électroniques
fixes.
6. Procédé selon la revendication 5, dans lequel les données de capteur d'émotion déterminées
pour être reçues de la pluralité de dispositifs électroniques mobiles sont pondérées
différemment des données de capteur d'émotion déterminées pour être reçues de la pluralité
de dispositifs électroniques fixes lors de la détermination du premier profil d'émotion.
7. Procédé selon la revendication 1, comprenant en outre :
la détermination, par le système informatique, d'un second contexte dans la pluralité
de contextes ;
la détermination, par le système informatique, d'une seconde partie des données de
capteur d'émotion déterminée pour être reçue d'une seconde partie de la pluralité
de dispositifs électroniques distribués (210) ; et
la détermination, par le système informatique, d'un second profil d'émotion pour le
second contexte en fonction de la seconde partie des données de capteur d'émotion,
le second profil d'émotion comprenant une description d'une seconde émotion associée
au second contexte.
8. Procédé selon la revendication 7, dans lequel le premier contexte comprend une première
période pendant un événement, et le second contexte comprend une seconde période pendant
l'événement.
9. Procédé selon la revendication 7, dans lequel le premier contexte comprend un premier
emplacement, et le second contexte comprend un second emplacement.
10. Procédé selon la revendication 7, comprenant en outre :
la détermination, par le système informatique, d'une différence entre le premier profil
d'émotion et le second profil d'émotion, la différence entre le premier profil d'émotion
et le second profil d'émotion décrivant une tendance de profil d'émotion liée au premier
contexte et au second contexte ; et
la sortie, par le système informatique, de la différence entre le premier profil d'émotion
et le second profil d'émotion.
11. Procédé selon la revendication 7, dans lequel la seconde partie de la pluralité de
dispositifs électroniques distribués (210) est déterminée à partir de la première
partie de la pluralité de dispositifs électroniques distribués.
12. Procédé selon la revendication 11, dans lequel le premier contexte comprend un premier
emplacement, et le second contexte comprend un second emplacement, et dans lequel
une partie du premier emplacement comprend le second emplacement.
13. Procédé selon la revendication 1, dans lequel le premier contexte comprend des données
d'événement auxquelles la première partie de la pluralité de dispositifs électroniques
distribués (210) était présente pour détecter les informations sur lesquelles les
données du capteur d'émotion sont basées.
14. Procédé selon la revendication 13, dans lequel les données d'événement comprennent
une période particulière associée à un emplacement particulier.
15. Procédé selon la revendication 1, dans lequel le premier contexte comprend une région
géographique déterminée dynamiquement dans laquelle la première partie de la pluralité
de dispositifs électroniques distribués (210) était située pour détecter les informations
sur lesquelles les données du capteur d'émotion sont basées.
16. Procédé selon la revendication 1, comprenant en outre la réception, par le système
informatique, de données de calendrier d'événements, les données de calendrier d'événements
comprenant une pluralité de dates et une pluralité d'emplacements associés, et la
détermination du premier contexte comprenant le référencement des données de calendrier
d'événement.
17. Procédé selon la revendication 1, comprenant en outre la sortie, par le système informatique,
du premier profil d'émotion.
18. Support de stockage non transitoire lisible par ordinateur contenant des instructions
qui, une fois exécutées, commandent l'ordinateur d'un appareil électronique afin d'effectuer
les étapes suivantes :
recevoir des données de capteur d'émotion d'une pluralité de dispositifs électroniques
distribués (210) pour une pluralité de contextes, chacun de la pluralité de contextes
correspondant à un ou plusieurs d'une zone géographique et d'une période, les données
de capteur d'émotion comprenant des indicateurs d'émotion implicites en fonction d'informations
détectées par la pluralité de dispositifs électroniques distribués pour une pluralité
de contextes, et les données de capteur d'émotion comprenant en outre des descripteurs
d'émotion explicites en fonction des descriptions d'émotions rapportées par l'utilisateur
pour la pluralité de contextes ;
déterminer un premier contexte dans la pluralité de contextes ;
déterminer une première partie des données de capteur d'émotion déterminée pour être
reçue d'une première partie de la pluralité de dispositifs électroniques distribués,
la première partie des données de capteur d'émotion étant basée sur des informations
détectées pour le premier contexte ;
déterminer un premier profil d'émotion pour le premier contexte en fonction de la
première partie des données de capteur d'émotion, la détermination comprenant la pondération
des indicateurs d'émotion implicites différemment des descripteurs d'émotion explicites
en fonction au moins de la fiabilité perçue des indicateurs d'émotion implicites et
des descripteurs d'émotion explicites, le premier profil d'émotion comprenant une
description d'une émotion associée au premier contexte ;
suivre, à l'aide d'un profil de tendance émotionnelle (1110), un changement émotionnel
associé à un individu passant d'un contexte parmi la pluralité de contextes à un autre
parmi la pluralité de contextes ; et
prédire, à l'aide du profil de tendance émotionnel et le premier profil d'émotion,
un changement émotionnel prédit associé à un utilisateur individuel passant d'un contexte
parmi la pluralité de contextes au premier contexte.
19. Dispositif électronique comprenant un ordinateur et le support de stockage non transitoire
lisible par ordinateur selon la revendication 18.